I’m working on a computer science question and need an explanation to help me learn.

A:

Data mining entails a process or method of extracting mines as interesting patterns from a large amount of data to facilitate the decision-making process.

Is it another hype?

No, data mining is not another hype. However, data mining’s core need is based on a wide variety of data and the need to transform it into useful information that organizations and individuals can use to make relevant decisions or analyses (Radhika & Masood, 2021). Therefore, data mining results from the evolution of information technology.

Steps involved in data mining

Data mining involves numerous steps towards the attainment of core set objectives such as,

Data integration- is the first step that involves a combination of multiple sources of data.

Data selection- the step entails retrieval of data relevant to analysis from the available database.

Data transformation- the step involves consolidating or transforming data into various forms that are appropriate for data mining.

Data mining- is an equally relevant step that entails applying intelligent and effective methods in extracting data patterns.

Pattern evaluation- is a method that involves the assessment of data patterns. As a result, it is possible to retrieve data relevant to the analysis from a database.

Lastly, knowledge presentation- is a critical step that involves the presentation of derived information. It entails the use of knowledge and visualization representation techniques in presenting mined knowledge to the selected users.

Is it a simple transformation or application of technology developed from the database, statistics, machine learning, and pattern recognition?

No, data mining entails more than just a simple transformation of technology developed from the various databases, machine learning, and statistics (Peng et al., 2017). Instead, it is a form of integration than just a simple transformation of various techniques from various disciplines, such as signal processing, statistics, database technology, high-performance computing, data visualization, and neural networks.

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B:

Data mining is a process of finding out the important information like the unknown patterns, hidden insights about the raw data by examining and analyzing it(Frankenfield, 2020). As per (Frankenfield, 2020),This does make it possible to find out the important information out of the loads of information, as due to the loads of information there can be possibility that the important information can be missed and in order to focus on important information data analysis is crucial(Frankenfield, 2020). In whereas purposes a data analysis can be used. For example, manufacturing companies keep track of the data like runtime, downtime and queue of the work for the different machinery present in the company(Frankenfield, 2020). They do it because with the help of analyzing the data one can predict the workload during the peak times. As per (Brownlee, 2020), The steps involved in knowledge discovery process are, it does starts with the cleaning the data stored from the databases as it is vital remove the noise and inconsistent data present in the raw data. The second step is data integration which means that combining the multiple data sources. As per (Brownlee, 2020), The other step is the data selection, in this phase the data required to the analysis are retrieved from the database. Another step is data transformation, in this step the data are transformed and aggregated into the formats appropriate for the data mining operations and aggregation options. As per (Brownlee, 2020), Another phase is the data mining in which the methods are applied to the data to extract data patterns. After that pattern evaluation is done to identify the patterns showing the knowledge based on the interesting measures. Knowledge presentation where the visualization are being prepared to present the data mining results to the users. Data mining is not just a simple transformation from database, statistics, machine learning. It is more than that, it does requires integration of data rather than a simple transformation of techniques from multiple areas like statistics, databases, machine learning, pattern recognition.

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Reference Books :

1.Chapple, Mike, Ballad, Bill, Ballad, Tricia, and Banks, Erin K. Access Control, Authentication, and Public Key Infrastructure, Second Edition. Jones & Bartlett Learning, 2016, ISBN: 978-1-284-03159-1

2.Rhodes-Ousley, Mark. The Complete Reference to Information Security, Second Edition. McGraw-Hill, 2013.